Universiteit van Amsterdam
The Financial Crisis and the Diversification Effect of European
Mergers and Acquisitions
Bachelor Thesis
Faculty Economics and Business Specialization Finance
Max Veldkamp 10052070
February 14, 2014
Abstract
This research studies a sample of 98 European mergers and acquisitions during a boom and bust period, also known as the financial crisis. The effect of the crisis on the relation between diversification and the abnormal returns of target shareholders is examined. Using event study methodology significant positive abnormal returns are found for shareholders of diversified and focused firms in both periods. The regression output partially supports my hypotheses as the crisis is significant in the determination of target abnormal returns. However, against expectations diversification and a few other variables do not play a significant role in the determination of target abnormal returns. So the suggestion that diversified firms experience financial benefits and investment advantages during a crisis and so increase their value is rejected.
Table of Contents
Abstract 2
Table of Contents 3
1. Introduction 4
2. Literature Review and Hypothesis Development 6
2.1 Value Creation 6
2.2 Diversification Clarified 6
2.3 Diversified and Financially Constrained 8
3. Data and Methodology 10
3.1 Data and Sample Selection 10
3.2 Methodology 11
3.3 Regression and Variables 12
4. Descriptive Statistics 15
5. Results 17
5.1 Event Study Results 17
5.2 Regression Results 19
6. Conclusion 20
7. Recommendations and Discussion 21
References Appendix
I. Introduction
Previous research has provided extensive evidence that under normal economic circumstances target firm shareholders can earn significant and positive abnormal returns by acquisitions. Wansley et al. (1983) and Campa & Hernando (2004) were the first to show this for the US respectively Europe. It is also documented that mergers and acquisitions (M&As) show a cyclical pattern and thus come in waves (Golbe & White, 1993). Literature stated six merger waves so far: those of the 1900s, the 1920s, the 1960s, the 1980s, the 1990s and the one which took place from 2003 -2010.
There are two main explanations for the origin of merger waves. The first statement includes the non-neoclassical or behavioral explanation in which the M&A activity is driven by the under- and overvaluation of the market stock (Shleifer & Vishny, 2003 and Rhodes-Kropf & Viswanathan, 2004). Two theories/hypotheses arise from this inefficient market: the ‘market-driven acquisition theory/ overvaluation hypothesis (OVH)’ and the ‘agency-driven acquisition theory/managerial-discretion hypothesis (MDH)’. On the other hand however, Andrade et al. (2001) support the neoclassical explanation in which is proclaimed that merger waves are the result of shocks in the business and economic environment, the so called ‘economic shock theory/industry shock hypothesis (ISH)’. A part of the sixth merger wave, from 2003 to 2007, is explained by Mueller & Shiereck (2011) through a rapid economic growth, overvaluation of the market’s stock and globalization. This part introduces the participation of private equity and other invest funds which increased the competition in the M&A world. The sixth wave was also characterized by something else, the 2007 – 2010 financial crisis. Due to the crisis the sixth merger wave was divided in a boom and bust period.
The financial crisis is seen as the second largest economic turmoil with a global impact after the Great Depression and also had an great impact on the global M&A activity. With the introduction of financial constraints the willingness of banks to lend money fell, liquidity decreased and less acquisition-funds remained. This influenced M&A activity all over Europe. Besides that is also changed the beliefs concerning the value of corporate diversification.
A survey about diversification in the past half-century is brought forward by Servaes (1996). In the 60’s and the early 70’s there was a trend towards conglomeration, as from the 80’s on the trend contradicted and focus regained its positive view. However the financial crisis started a debate again in which Kuppuswamy & Villalonga (2010) state that conglomerates might be ready for a comeback.
According to Servaes (1996) corporate focus has dominated diversification until the end of the past century. In the beliefs of Kuppuswamy & Villalonga (2010) the financial crisis changed this view and
a diversification premium exists under certain circumstances. In the wake of an economic downturn conglomerates face investment advantages. Diversified firms are better in handling decreasing liquidity than focused firms. These firms can reallocate financial resources to or from suffering divisions. So in the line of thought from Kuppuswamy & Villalonga, it is possible that conglomerates are likely to perform better than non-conglomerates in times of financial constraints.
The existing literature on diversification is comprehensive. On the other hand few literature on diversification relating to firm performance distinguishes between boom and bust periods. Kingsley (2006) and Papadakis & Thanos (2008) proved the importance to differentiate between acquisition announcements during different economic circumstances. Their research showed that M&A activity performed during crises is variant of those performed during non-crisis periods. Hence the main features of the domestic and cross-border acquisitions and so the target firm abnormal returns could diversify. By involving the element of diversification, the distinction between conglomerate and non-conglomerate deals, in the performance of target firms this thesis will contribute to recent studies. The aim of this study is to disclose the differences in the relation between the choice to focus or diversify and target firm abnormal returns of M&A activity in Europe during a boom period (2004-2007) and a bust period (2007-2010).
As most of the existing M&A literature is confined to the US the conclusions regarding corporate diversification are derived from the evidence of this continent. By emphasizing on European M&A activity and corporate diversification this thesis contributes to prior literature.
II. Literature Review and Hypothesis Development
2.1 Value Creation
European studies on M&A activity started in the early 2000s. In an efficient market the markets reacts immediately to the announcement of a merger or acquisition. Mergers and acquisitions can create net value for target shareholders under normal economic circumstances (Campa & Hernando, 2004). The main goal of mergers and acquisitions is the development of synergies which can lead to gains for the involved firms. From the numerous sources of gains financial and operating synergies and management incentives are of important value (Grinblatt & Titman, 1998).
The value creation by means of growth and synergies is the first source of gains. This can be summarized mathematically as follows V( C ) > V( A ) + V ( T ). When the unlevered cash flows of the individual firms are less than the unlevered cash flows of the combined firm, there are operating synergies created by the merger or acquisition. Operating synergies can be divided in revenue and cost synergies of which cost synergies often are the result of economies of scope and scale. These economies of scope derive mostly from the combination of complementary competences in related mergers. Economies of scale arise because combined the firms produce lower average costs. Meanwhile revenue synergies are the outcome of rising profits through sales.
The next possible source underlying value creation is the observed benefit from growing market power. Controlling the price or quantity as a market participant are requirements to obtain market power. Increasing market power provides the possibility to outperform competitors and could yield entry thresholds to potential competitors. Management incentives like compensation for increasing sales or firm size can be bound to market power. In this case mergers can occur without maximizing value for the shareholders of the concerning firms.
Lastly M&As can create value by diversifying. Diversified firms experience more stable incoming cash flows and as a result the profit fluctuates less. Next to this there are other benefits which arise from diversifying mergers like more efficient internal capital markets and higher debt levels. These benefits will be explained further on in the literature.
2.2 Diversification Clarified
There is extensive research on the issue of corporate diversification. In fact Martin & Sayrak (2003) summarize different studies on this subject which shows the pros and cons of diversification. As argued by, amongst others, Lang & Stulz (1994), Berger & Ofek (1995), Servaes (1996), Lins & Servaes (1999) and Doukas et al. (2002) diversified firms trade at a discount, which implies that diversified takeovers destroy shareholders’ wealth. However Villalonga (2004) shows that US diversified firms trade at a premium when using data at the establishment level. During an economic downturn, it is noticed that diversified firms have financing and investment benefits over focused firms (Kuppuswamy & Villalonga, 2010). This could mean that the value of diversification varies with financial constraints and economic conditions. Khanna & Palepu (1997), as well as Matsasuka & Nanda (2002) already suggested before that the diversification advantages are greater in the presence of financial constraints and emerging or less-developed capital markets. Based on this I expect that the diversified target firms generate higher abnormal returns than the focused target firms in periods of economic downturn. Since existing studies on M&A establish that European targets gain significant positive abnormal returns the following set of hypotheses is tested first:
H1: The performance of target firms in European mergers and acquisitions is significantly different from zero both in a boom and bust period.
H1a: Target firms generate significant positive abnormal returns in a boom period. H1b: Target firms generate significant positive abnormal returns in a bust period.
To investigate the effect of the choice between diversification or focus on the abnormal returns of European targets in both a boom period and a bust period I set up a second set of hypotheses.
H2: There is a significant difference in the relation between the abnormal returns of European target firms and diversification between a boom period and a bust period.
H2a: The target abnormal returns are significantly related to diversification.
H2b: The abnormal returns of diversified target firms are significantly related to the crisis period.
Prior research introduces different causes for the discount at which conglomerates trade. This discount is inter alia, due to the incompetence to capitalize on competitive advantages (Porter, 1980) and core competencies (Hamel & Prahalad, 1990). Likewise contributing to the loss in value is the failure of the firms debt capacity and internal capital markets (Comment & Jarrell, 1995 as well as Scharfstein, 1998).
Firms are unable to handle the higher levels of debt and lack competencies to efficiently allocate the incoming cash flows from different divisions. It is also proclaimed that conglomerates acquire target firms that already are discounted (Graham, Lemmon & Wolf, 2002). Laeven & Levine (2007) provide another origin for the discount of financial conglomerates. Agency problems causes participation in multiple activities which do not lead to economies of scope and so reduces firm value. Kuppuswamy & Villalonga (2010) approach the loss in value differently, it is a necessary cost which hedges the firm against bad states of the world.
Contrastingly, there is also academic work which discloses the benefits of diversification. So do Williamson (1970) and Stein (1997) indicate that diversification creates or expands the internal capital markets for individual firms. The financing of internal capital markets benefits from the cash flows of diversified firms and the firms’ value on their turn may benefit from the use of internal capital markets as financing mechanism. This could lead to opportunities of evading the inefficiencies of the external capital markets (Williamson, 1970). First of all, in the presence of internal capital markets managers can exercise more control when making investment decisions, preferably than less well-informed investors of the external capital market deciding where the firm needs to invest (Weston, 1970). Stein (1997) supports this argument by stating that managers with asymmetric information can make better investment decisions and thus can create firm value. In short, a diversified firm is better able to allocate funds from inefficient divisions to efficient divisions and create possible shareholder value. Second, it is less expensive to raise funds derived from an internal capital market than raising it externally. The problem of asymmetric information, when selling shares to the public is avoided and there are no transactions costs associated with the sale of shares (Hadlock et al., 2001).
2.3 Diversified and Financially Constrained
As mentioned before, Williamson (1970) points out that evading inefficiencies in the external capital market is one of the key rationales behind diversifying behavior. The internal capital market arisen from diversification may diminish the information asymmetry between managers and external investors (Weston, 1970). Besides this, a constantly higher level of debt (Lewellen, 1971), economics of scope (Teece, 1980) and higher debt capacity (Shleifer & Vishny, 1992) can be the result of diversification. Kuppuswamy & Villalonga (2010) notice that these benefits of diversification can be greater in the presence of a financial crisis. They allude to the more money effect and the smarter money effect. The more money effect includes the higher level of debt addressed first by Lewellen (1971) and the higher debt capacity addressed by Shleifer & Vishny (1992). Because of the decreasing volatility of incoherent
cash flows the default risk reduces and the conglomerates can lever up. The smarter money effect refers to the potential benefits of the internal capital markets of diversified firms. It applies here that reallocating funds from inefficient divisions to efficient divisions is the main benefit (Stein, 1997). Stein (1997) shows that this reallocation of funds is most efficient in times of economic turmoil as several divisions compete for the scarce financial resources and the managers’ incentives to choose for the most rewarding divisions increases.
It should be noted that mergers and acquisitions are a way to diversify. Mergers and acquisitions are part of a model in which, according to Shleifer & Vishny (2003), M&A’s are induced by the under- or overvaluation of stock and the market’s expectation about the possible synergies that originate from this. Shleifer & Vishny (2003) claim that it is essential to assume that external capital markets are inefficient so there is room for the mispricing of shares. Meanwhile upper level management does not face the problem of asymmetric information. Managers know the exact condition and value of the firm. So M&A’s actually represent arbitrage opportunities discovered by managers. Issues like the choice of payment method, the valuation effects of mergers, merger waves and who acquires whom is accounted by this arbitrage opportunity. Therefore, the merger waves can be accounted by the fact that managers benefit from the existence of asymmetric information. In other words, supported by Weston (1970) and Stein (1997), internal capital markets are more efficient in allocating resources to investment projects than external financial markets. With respect to this thesis, it is interesting to see how both external and internal conditions (opportunities) drive the decisions to either diversify or focus.
III. Data and Methodology
3.1 Data and Sample Selection
Data Retrieval
This study examines a sample of European mergers and acquisitions performed during a pre-crisis period and the global financial crisis. The Bureau van Dijk Zephyr databank is consulted to characterize the M&A deals. The characteristics of the selected M&A’s from Zephyr include the name and country of the target firm, the announcement date, the SIC-code(s), the deal type and the deal status. Table I on page 11 shows the composition of the sample by diversification and time period. The Thompson Reuters Datastream financial database and Wharton Research Data Services (WRDS) are used for the additional transaction data. Berger & Ofek (1995) and Kuppuswamy & Villalonga (2010) also used WRDS as database in comparable studies. The target firm returns and the market return index are drawn from the Datastream database. As a benchmark the S&P Euro index is selected. Additional transaction data controlled for is market value, Tobin’s Q, total assets, cash holdings, leverage, free cash flows, return on assets, return on equity and capital expenditures which are retrieved from Compustat and Datastream combined.
Time Selection
01/01/2004 – 30/06/2007 and 01/07/2007 – 31/12/2010 are the chosen intervals which refer to the boom respectively bust period. The period chosen for the crisis is equal to previous research on the financial crisis. In the US it is publicly accepted that July 2007 is the start of the financial crisis. Besides that, in July 2007 the German IKB Bank was the first European enterprise which was affected by the US crisis (Brunnermeier, 2009). So based on this and previous research it will be assumed that the starting date for the European financial crisis is similar. Although current European M&A activity is still affected by the crisis the ending date of the crisis period will be set at December 2010, so both periods are consistent in length.
Table I
Composition of the sample of European acquisitions
3.2 Methodology
This thesis applies an event study and literature review to investigate the relation between target stock returns and diversification under different economic circumstances, i.e. during a pre-crisis and crisis period. This section will discuss the applied event study methodology and the next section will elaborate on the regression analyses. Chapter V and VI will provide the empirical results obtained from the described methods in this chapter.
Event Study Methodology
The event study methodology examines whether target market reactions are influenced by the choice between diversification or focus during a boom and bust period. The market reaction of target shares to takeover announcements is determined by abnormal returns around the announcement day. To compute these abnormal returns , the market adjusted- and mean adjusted model will be applied. After that this study inquires whether it varies for a boom and bust period. Assumed is the semi strong efficient-market hypothesis which means that stock prices reflect all past publicly available information and change immediately when new public information comes available.
The estimation window to compute the benchmark returns during the pre-crisis and crisis period will be set at 201 days, [-215 ; -15]; a period covering 215 days until 15 days before the announcement. A relatively large estimation window is chosen to retrieve unbiased estimates. The event window is for the realized returns. This thesis uses 16-day, 3-day, 11-day and 31-day event windows; [-15 ; 0], [-1 ; +1], [-5 ; +5] and [-15 ; +15]. Thereby possible pre- and after-announcements effects are taken into account.
It is already stated that the market reaction of target shares to takeover announcements will be determined by abnormal returns around the announcement day. The market adjusted- and mean
Period Diversified Focused Total
Boom Period 26 18 44 Percentage of Sample 26.53% 18.37% 44.90% Crisis Period 33 21 54 Percentage of Sample 33.67% 21.43% 55.10% Total 59 39 98 Percentage of Sample 60.20% 39.80% 100.00% 11
adjusted model will be used for this. The market adjusted model compares the return of a target firm on a specific day in the event window with the actual market return on that day. So the abnormal return of the ith target firm on day t is denoted as:
ARi, t = Ri, t – Rm, t where
Ri, t = Actual return of ith firm on day t of the event window Rm, t = Actual market return of the S&P Euro on day t
The mean adjusted model computes the abnormal return by comparing the actual return of a target firm on a specific day in the event window with the average daily return in the estimation window. The average daily return in the estimation window is obtained by calculating the mean of the normal returns of the target firm in the estimation window. By subtracting the latter from the actual return on day t in the event window gives the abnormal return of the ith target firm. The abnormal return of the ith firm on day t is obtained through the following equation:
ARi, t = Ri, t – µi where
Ri, t = Actual return of ith firm on day t of the event window µi = Mean of returns of ith firm in the estimation window
After calculating the abnormal returns of the ith firm on day t of the event window with both models the cumulated abnormal returns (CAR) on day t are computed. The CAR’s for each event window are then set and subsequently converted in cumulated average abnormal returns (CAAR) for each event window. To test if the CAAR’s are reliable and significantly different from zero a standard parametric t test is used under the assumption of normality.
4.3 Regressions and Variables
In this study the regression examines the variables which determine the abnormal returns and especially concentrate on the effect of diversification. In this regression is also controlled for potential explanatory variables. The following regression will be estimated with Ordinary Least Squares (OLS). The main objective of this regression is to estimate the explanatory power of the crisis and the choice between focus or diversification in the determination of target firm abnormal returns. The regression contains two dummy variables: CRISIS (equal to 0 for the boom period and 1 for the bust period) and DIVERSIFICATION (equal to 0 for focus and 1 for diversification). DIVERSIFICATION is measured using four-digit SIC-codes. When the codes deviate from each other than the M&A’s are diversified. Furthermore an interaction term between the crisis dummy variable and diversification dummy variable is added: CRISIS*DIVERSIFICATION.
CAR = β0 + β1CRISIS + β2DIVERSIFICATION + β3TOBINSQ + β4MARKETVALUE + β5CASHHOLDINGSTOASSETS + β6LEVERAGE + β7FCFTOASSETS + β8ROA + β9ROE + β10CAPEXTOASSETS + β11CRISIS*DIVERSIFICATION + ε
Besides the independent variables there are eight other firm-specific variables in the regression which need to be controlled for. TOBINSQ is obtained through the ratio of [Market Value of Equity + Book Value of Liabilities] to [Book Value of Equity + Book Value of Liabilities]. This variable makes it possible to measure how a firm creates value in excess of its deployed assets. Tobin’s Q is used inter alia, to compute the value of diversification (Lang and Stulz, 1994) and to designate investment opportunities (Berger and Ofek, 1995).
MARKETVALUE is defined as the individual share price times the number of shares outstanding. This metric is used to express the size of a firm which affects the choice between focus or diversification.
Cash holdings are an important factor to persist the crisis (Kuppuswamy & Villalonga, 2010). CASHHOLDINGSTOASSETS is defined as the ratio of a firm’s cash holdings to total assets. In the absence of favorable investment opportunities within the firm, cash surplus should be transferred to the investors according to perfect capital markets. Nevertheless Jensen (1986) forecasts that a part of these cash holdings is spend on value destroying diversification. So cash holdings might be an indicator for diversifying behavior. In addition Duchin (2010) proves that conglomerates holds significantly less cash than non-conglomerates.
LEVERAGE is the ratio of total debt to book value of assets or in other words, total assets. According to Servaes (1996) leverage determines firm value through profitability.
FCFTOASSETS (variable measuring the free cash flows of the firm) presents the cash in excess of the necessary resources to retain the assets. This cash can be used to increase shareholder value.
According to Servaes (1996) managers diversify to secure the value of human capital, to raise private benefits and to increase job security. All these measurements are linked to firm performance through executive compensation plans. Therefore, diversification is partially linked to firm performance and this explains the use of ROA and ROE as control variables. ROA (return on assets) is computed by the ratio of net income to total assets. Return on equity (ROE) is obtained through the ratio of net income to shareholders’ equity. Finally CAPEXTOASSETS is measured by the ratio of capital expenditures to total assets. Capital expenditures stand for the investment policy and must, for the same reasons as leverage, be controlled for.
The financial crisis could affect certain industries to a larger extent than others. This could denote the existence of industry effects which should be controlled for. But Lang & Stulz (1994) prove that these industry effects are insignificant and so do not have to be controlled for. Further are the metrics Tobin’s Q , cash holdings, leverage, free cash flows, return on assets, return on equity and capital expenditures scaled which implies that these are not subject to differences in firm size.
IV. Descriptive Statistics
This part provides statistics that define the selected sample in different ways. It gives a better understanding of the data. The mean and median statistics circumscribe the distribution of the important variables, furthermore these statistics show how the variables diverge for diversified and focused firms. Table II in the appendix shows the number of observations, means, medians, standard deviations of all the variables used in the regression. Further there is made a comparison between the diversified and focused firms. The difference in means between the two subsamples is provided in the ‘Difference in Mean’ column. A two sample mean comparison test is done to test the significance of the differences.
In contrast with the literature this table shows that focused firms have lower Tobin’s Q scores than diversified firms on average. Besides that on average the focused firms have higher cash holdings, total assets, leverage, return on assets, return on equity and capital expenditures than diversified firms which is also in contrast with the existing literature inter alia, Kuppuswamy & Villalonga (2010). This could be due to the fact there are some relatively large focused firms in the sample. In the same line of thoughts these results could also be caused by the presence of a few relatively small diversified firms in the sample. Another explanation could be found in the Zephyr search criteria, where is chosen for the criterion that both the acquirer and target must be located in Europe instead of only the target. It must be noted that all of the above differences in means are not significant. However the differences in means are significant for the market value, total debt and free cash flows. These three variables are also on average higher for focused firms than diversified and again this contradicts recent studies on diversification. According to Kuppuswamy & Villalonga (2010) conglomerates are larger than non-conglomerates, have higher debt levels and generate higher cash flows.
Table III in the appendix displays how the means of the different variables change over time. For both diversified as focused firms it appears that the common movements over time coincide. Only the variables Tobin’s Q and return on equity deviate from these results. Nevertheless more essential is how the differences in means between diversified and focused firms change over time. The scope of these differences provides a view on the relative strength of diversified firms versus focused firms. In line with Kuppuswamy & Villalonga (2010) it is expected that the differences in means develop in favour of the conglomerates during the crisis. Moreover the diversified firms outperform the focused firms. From this table it becomes clear that the diversified firms underperform on all aspects except for the Tobin’s Q and return on equity. Further these numbers confirm that the differences in means change in favor of
the diversified firms. Again the found discrepancies could be explained by the search criteria in Zephyr and the presence of relatively small diversified or relatively large focused firms.
V. Results
5.1 Event Study Results
Using the event study results it is possible to test the first set of hypotheses defined in section 2.2 by looking at the significance and values of the CAAR’s. Both hypotheses, H1a and H1b, are supported by the results of the event study. These results are then converted back into firm specific CAR’s which are used as input for the regression analyses.
The results of the market adjusted model are represented in Table IVA on page 18. The model consists of positive cumulative average abnormal returns during four event windows. So positive CAAR’s are generated by each subset: diversified M&A’s during the boom period, focused M&A’s during the boom period, diversified M&A’s during the bust period and focused M&A’s during the bust period.
Hypothesis H1a is supported by the outcomes of the boom period as this period denotes positive abnormal returns for both diversified and focused firms for all event windows. However the outcomes do not support prior research in which is stated that diversified firms trade with a discount. In accordance with these results target shareholders gain significant abnormal returns of 5.09% for focused M&A’s in the 11-day event window. Positive CAAR’s are also found for the diversified M&A’s during a boom in all event windows, though these abnormal returns are insignificant.
The outcomes of the crisis period provide even stronger evidence in favor of hypothesis H1b. There are positive abnormal returns for target shareholders in diversified and focused M&A’s. Target shareholders of diversified firms gain significant positive abnormal returns of 9.49% to 18.33% to 22.64% in the 3-day, 11-day and one-month event window respectively. Significant results are reported for target shareholders of focused firms in all event windows, varying from 6.64% in the 3-day event window to 15.58% in the one-month event window. The above outcomes are partially supported by previous studies as the value of diversification increases during the crisis period.
Table IVB on page 18 displays the results of the mean adjusted model. The evidence is almost similar to the market adjusted model, except there is no significant result in the boom period and target shareholders of diversified firms during the boom period gain negative returns in the 16-day event window. On the other hand diversified firms during the crisis period denote significant abnormal returns of 8.83% to 16.73% to 19.86% in the 3-day, 11-day and one-month event window respectively. Target shareholders of focused firms during the crisis gain significant abnormal returns in all event windows, ranging from 5.93% to 11.71% in the 3-day and one-month event window respectively.
Table IVA and IVB
Event study results market adjusted- and mean adjusted model
Event study results for the market adjusted- and mean model denoted as the cumulative average abnormal returns and t statistics calculated by the methodology in section 3.2.
Diversified M&A's during
boom Focused M&A's during boom Diversified M&A's during crisis Focused M&A's during crisis
Event Window CAAR t Stat CAAR t Stat CAAR t Stat CAAR t Stat
(-15;15) 4.76% 0.944 3.89% 1.521 22.64%** 2.018 15.58%* 3.048
(-15;0) 0.29% 0.084 2.84% 0.979 16.39% 1.567 11.25%* 2.784
(-5;5) 3.87% 1.010 5.09%*** 1.827 18.33%* 2.751 9.49%* 3.252
(-1;1) 2.31% 1.149 2.36% 0.922 9.49%** 2.524 6.64%* 3.035
Diversified M&A's during boom
Focused M&A's during boom
Diversified M&A's during crisis
Focused M&A's during crisis
Event Window CAAR t Stat CAAR t Stat CAAR t Stat CAAR t Stat
(-15;15) 1.84% 0.380 0.53% 0.120 19.86%*** 1.864 11.71%** 2.093
(-15;0) (-0.37%) -0.109 1.22% 0.351 16.11% 1.568 8.80%*** 1.931
(-5;5) 2.03% 0.556 4.15% 1.307 16.73%* 2.589 7.70%* 2.816
(-1;1) 0.71% 0.455 3.04% 1.206 8.83%** 2.375 5.93%* 2.679
5.2 Regression Results
By looking at the explanatory powers of diversification and the crisis in the ascertainment of the target abnormal returns the regression results assist in analyzing the hypotheses H2a and H2b. Table V in the appendix presents the outcomes of the regression output. The regression output is in support of hypothesis H2b, though hypothesis H2a is not supported.
Table V in the appendix holds the regression output for the market adjusted model and the mean adjusted model. Both the models show that the presence of a crisis has a significant effect on the cumulative abnormal returns (CAR). For the market adjusted model the relation between CRISIS and CAR is statistically significant at the 10% level with a coefficient of 11,645. Meanwhile the mean adjusted model contains a statistically significant CRISIS variable at the 5% level with a coefficient of 14,374. The direction of these effects is supported as these are both positive. On the other hand the CAR is not significantly affected by the dummy variable DIVERSIFICATION in either model. However, the direction of the coefficients is similar and positive. Hence hypothesis H2a is not supported, but hypothesis H2b is supported. The abnormal returns of diversified and focused target firms are significantly affected by the crisis. Other significant variables in the determination of target abnormal returns comprise Tobin’s Q (-) at the 5% level and capital expenditures to total assets (+) at the 1% level. Contrary to expectations and prior literature the variable TOBINSQ has a negative instead of a positive coefficient which has a significant impact on the cumulative abnormal return. Furthermore I expected the variables LEVERAGE, CASHHOLDINGSTOASSETS and FCFTOASSETS to be statistically significant. The regression outcomes did not change after adding other control variables as LOGTOASSETS and LEVERAGE^2.
VI. Conclusion
This thesis examines a sample of 98 European mergers and acquisitions during a timeframe from 01/01/2004 to 31/12/2010, which includes a boom period and a bust period. The concerning bust period is also known as the financial crisis. This thesis contributes to recent M&A studies by involving the crisis in the relation between diversification and the abnormal returns of target firms. Furthermore the emphasis on the European M&A market contributes as most of the prior research is confined to the US and the UK.
In the event study methodology the market adjusted- and mean adjusted model are applied to compose the cumulative abnormal returns (CAR’s) for shareholders of target firms during a boom phase and a bust phase. The CAR’s are then converted in cumulative average abnormal returns (CAAR’s) for each of the four event windows. The outcomes of this methodology provide evidence in support of hypothesis H1 as the market model shows significant CAAR’s for both type of firms in each period. The mean adjusted model only displays statistically significant CAAR’s in the crisis period.
Next the previously obtained cumulative abnormal returns (CAR’s) are used as dependent variable in the ordinary least squares regression to find evidence in support of hypotheses H2a and H2b. The outcomes are in support of hypothesis H2b as the gains of diversified target shareholder are significantly affected by the financial crisis. However, hypothesis H2b is not supported since the CAR’s are not significantly associated with the variable DIVERSIFICATION. Also is the regression output not in line with expectations and prior diversification literature as leverage, cash holdings to total assets and free cash flows to total assets do not have a significant effect on the abnormal returns of target shareholders. Besides that the direction of the Tobin’s Q coefficient contradicts, amongst others, Kuppuswamy & Villalonga (2010) whom obtained a positive coefficient. All these findings suggest that diversified target firms generate significant abnormal returns during a crisis period. Nonetheless it seems that these diversified firms do not experience financial benefits and investment advantages during an economic downturn.
VII. Recommendations and Discussion
Several issues were encountered in the process of completing this thesis. It might be conducive for future studies to refer to these issues. First of all the used samples in this research are small which often induces insignificant results. Besides that it might reduce the strength of the conclusions. Future research should take this into account for instance by expanding the time horizon. Though the distinction between the boom and bust period should be considered, so it could be useful to extend the crisis period for example to the end of 2011 and spilt the time horizon in four phases: a pre-crisis, an early crisis, a late crisis and a post-crisis phase. This way the post-crisis effects are also accounted for. Another possibility to increase the size of the samples is to alter the search criteria in Zephyr. For instance only the target should be listed in Europe instead of both the acquirer and the target.
Second this thesis targets only the short-term effects of European mergers and acquisitions since the largest event window covers a one-month period. Future research might set to the long-term effects of merger and acquisitions announcements on target abnormal returns. This could be done in conjunction with expanding the time horizon.
Lastly, the outcomes might be influenced by various variables which are not controlled for in the regression. This could be variables linked to the crisis or to the non-rational behavior of agents during an economic downturn. For instance the shareholder stake of upper level management could affect the outcomes as diversification can become a part of the agency problem, for example raising private benefits through empire building. Future research should attempt to find these additional control variables.
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Appendix
Table II
Descriptive Statistics: Means
The table below displays summary statistics for variables in the sample. Besides summarizing the entire sample, there is made a distinction between diversified and focused firms. This distinction is based on diversification criteria, 4-digit SIC-codes. The differences in means are tested with a two sample mean comparison test, where *, ** and *** denote significance at the 1%, 5% and 10% levels, respectively.
Variable N Mean Median Deviation Standard Difference in Mean t Stat.
Tobin's Q 98 1.463 1.129 1.065 0.211 1.089 Diversified 59 1.547 Focused 39 1.336 Market Value 98 803.867 43.995 2389.274 (-1101.212)** -2.009 Diversified 59 365.630 Focused 39 1466.842 Cashholdings 98 130752.500 6400 530611.300 (-192116.530) -1.486 Diversified 59 54297.970 Focused 39 246414.500 Total Assets 98 2047923 123462 9064331 (-3566663.500) -1.586 Diversified 59 628536.500 Focused 39 4195200 Total Debt 98 514958.700 12983 2391600 1044046.27)*** -1.753 (-Diversified 59 99470.730 Focused 39 1143517 Leverage 98 0.203 0.154 0.221 (-0.029) -0.639 Diversified 59 0.192 Focused 39 0.221
Free Cash Flows 98 138615.500 4515 493325 (-237288.34)** -2.050
Diversified 59 44184.460
Focused 39 281472.800
Return on Assets 98 0.238 0.035 1.185 (-0.343) -1.248
Diversified 59 0.101 26
Focused 39 0.444
Return on Equity 98 0.669 0.090 2.730 (-0.210) -0.379
Diversified 59 0.585
Focused 39 0.795
Capital Expenditures to Total
Assets 98 0.066 0.019 0.171 (-0.025) -0.670
Diversified 59 0.056
Focused 39 0.081
Table III
Descriptive Statistics: Means over Time
The table below displays summary statistics for variables in the sample over time. Besides summarizing the entire sample, there is made a distinction between diversified and focused firms. This distinction is based on diversification criteria, 4-digit SIC-codes.
Period Boom Crisis
Mean Tobin's Q
Diversified 1.559 1.538
Focused 1.276 1.346
Difference 0.283 0.192
Mean Market Value
Diversified 730.437 78.206 Focused 1815.657 1082.990 Difference -1085.220 -1004.784 Mean Cashholdings Diversified 104884.300 14442.030 Focused 320767.400 168192.100 Difference -215883.100 -153750.070
Mean Total Assets
Diversified 1252811 136683.500
Focused 6134591 2242317
Difference -4881780 -2105633.50
Mean Total Debt
Diversified 168301.100 45240.730 Focused 1513189 754857 Difference -1344887.90 -709616.270 Mean Leverage Diversified 0.147 0.227 Focused 0.188 0.247 Difference -0.041 -0.020
Mean Free Cash Flows
Diversified 92088.730 6441.697
Focused 358847 198151.100
Difference -266758.270 -191709.403
Mean Return on Assets
Diversified 0.266 -0.028
Focused 0.857 0.055
Difference -0.591 -0.083
Mean Return on Equity
Diversified 0.259 0.843
Focused 1.537 0.094
Difference -1.278 0.748
Mean Net Income
Diversified 76338.150 689.909
Focused 6219284 9691.333
Difference -6142945.85 -9001.424
Mean Capital Expenditures to Total Assets
Diversified 0.050 0.060
Focused 0.053 0.103
Difference -0.003 -0.042
Table V
Regression Output Explaining the Target Abnormal Returns
The dependent variable is the cumulative abnormal return as computed by the market adjusted model and mean adjusted model. The regression results are obtained with OLS regressions, where *, ** and *** denote significance at the 1%, 5% and 10% levels, respectively.
Control Variables
Market Adjusted Model Mean Adjusted Model
Coef. t Stat. Coef. t Stat.
Crisis Dummy 11,645*** 1.870 14,374** 2.030
Diversification Dummy 3.778 0.640 4.542 0.680
Tobin’s Q (-4,018)*** -1.690 (-6,811)** -2.510
Market Value (-0,001) -0.690 0.000 -0.040
Cashholdings to Total Assets (-2,668) -0.200 (-5,023) -0.330
Leverage (-4,646) -0.470 (-12,722) -1.120
Freecashflows to Total Assets 10.096 0.890 (-0,172) -0.010
Return on Assets (-3,297) -1.400 (-1,500) -0.560
Return on Equity 1.133 1.190 0.801 0.740
Capital Expenditures to Total Assets (-19,145) -1.630 (-44,272)* -3.300
Interaction Dummy (-9,039) -1.140 (-7,706) -0.850 Constant 12,75112** 2.000 14,647** 2.010 Number of Observations 98 98 F( 11, 86) 1.500 2.270 Prob > F 0.146 0.018 R-squared 0.161 0.225 Adj R-squared 0.054 0.126 30
